I came across this calculation when I was reading about Recommender systems. The last column is the rating given by a particular user for a movie. The other columns of this matrix denote whether a particular actor appeared in the movie or not.

The first five attributes are Boolean, and the last is an integer "rating." Assume that the scale factor for the rating is α. Compute, as a function of α, the cosine distances between each pair of profiles. For each of α = 0, 0.5, 1, and 2, determine the cosine of the angle between each pair of vectors.

Matrix M has three rows and three columns, and the columns form an orthonormal basis. One of the columns is [2/7,3/7,6/7], and another is [6/7, 2/7, -3/7]. Let the third column be [x,y,z]. Since the length of the vector [x,y,z] must be 1, there is a constraint that x2+y2+z2 = 1. However, there are other constraints, and these other constraints can be used to deduce facts about the ratios among x, y, and z. Compute these ratios, and then identify one of them in the list below.

Orthonormality

The goal is to find two clusters in this graph using Spectral Clustering on the Laplacian matrix. Compute the Laplacian of this graph. Then compute the second eigen vector of the Laplacian (the one corresponding to the second smallest eigenvalue).

I think I understood the basic Datar-Gionis-Indyk-Motwani Algorithm which is explained in the book “Mining of massive datasets” by Jure Leskovec(Stanford Univ.),Anand Rajaraman(Milliway Labs) and Jeffrey D. Ullman(Stanford Univ.)

I will add more details later but the diagram below explains it. I used Tikz to draw this picture. I will check-in the tikz code to my github and post the link.

Suppose we are using the DGIM algorithm of Section 4.6.2 to estimate the number of 1's in suffixes of a sliding window of length 40. The current timestamp is 100.
Note: we are showing timestamps as absolute values, rather than modulo the window size, as DGIM would do.
Suppose that at times 101 through 105, 1's appear in the stream. Compute the set of buckets that would exist in the system at time 105.

This is the general market-basket problem. It is an algorithm to find how many items are frequently found across many shoppers’ baskets based on a threshold. The threshold is a minimum number of occurrences of a particular item. Items that are bought a certain number of times(threshold) are considered frequent.

These items can be singletons or pairs of items(doubletons) and tripletons and so on.

Imagine there are 100 baskets, numbered 1,2,...,100, and 100 items, similarly numbered. Item i is in basket j if and only if i divides j evenly. For example, basket 24 is the set of items {1,2,3,4,6,8,12,24}. Describe all the association rules that have 100% confidence. Which of the following rules has 100% confidence?

A brute-force R approach to solve such a problem. This is a small number of items. In fact such data mining algorithms deal with large quantities of data and a fixed amount of memory. One such algorithm is the A-priori algorithm.

Each of the if loop checks for a condition like this.

{8,10} -> 20

This checks if item 20 is always found in a basket that has items 8 and 10 or not.